Table \ref{{table_params}} contains the estimated parameters from the
Simulated Method of Moments. Overall the parameters seem reasonable.
The potential well draw parameters $(d_0,d_1)$ imply that the elasticity
of the number of potential project draws with respect to the gas price
is approximately {elasticity}.\footnote{{
I calculate this approximate elasticity by taking the number of potential
projects at the average gas price (\$5.83) in the sample. Here, a 1\%
({change_gas} cents) increase in the gas price would cause potential project
draws to increase by {change_draws}, resulting in an elasticity of {elasticity}.
}}

The estimated targeting parameter $\gamma$ is ${gamma_0}$. To get a sense of
where this lies between random search and directed search I consider the
probability that a complex well (I set $x_{{\text{{complex}}}}=2.0$)
targets its optimal match (which is a high-specification rig) at
approximately the average state:\footnote{{
Specifically, I choose the state halfway through the sample at January
2005 which is also between a boom and bust.
}}
\begin{{align*}}
\omega_t(y=\text{{high}}|x=\text{{complex}})=\begin{{cases}} 
{v_random} & \text{{random search}}\\
{v_partial} & \text{{estimated model}} \\
1 & \text{{directed search}}
\end{{cases}}
\end{{align*}}
The above example indicates the search technology that best rationalizes the data is
closer to random search than directed search. Since imposing assumptions on the
search technology has welfare implications, it is nevertheless still necessary to estimate
this targeting parameter flexibly.

For the remaining parameters (the matching efficiency and the mean and
standard deviation of potential projects) it is difficult to interpret
them in isolation. Therefore, I see how closely the model fits the data
overall.